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Cognitive fitness of cost-efficient brain functional networks
Open accessAbstract
The human brain's capacity for cognitive function is thought to depend on coordinated activity in sparsely connected, complex networks organized over many scales of space and time. Recent work has demonstrated that human brain networks constructed from neuroimaging data have economical small-world properties that confer high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of complex brain networks functioning at different frequencies can be related to behavioral performance on cognitive tasks. Here, we show that impaired accuracy of working memory could be related to suboptimal cost efficiency of brain functional networks operating in the classical β frequency band, 15–30 Hz. We analyzed brain functional networks derived from magnetoencephalography data recorded during working-memory task performance in 29 healthy volunteers and 28 people with schizophrenia. Networks functioning at higher frequencies had greater global cost efficiency than low-frequency networks in both groups. Superior task performance was positively correlated with global cost efficiency of the β-band network and specifically with cost efficiency of nodes in left lateral parietal and frontal areas. These results are consistent with biophysical models highlighting the importance of β-band oscillations for long-distance functional connections in brain networks and with pathophysiological models of schizophrenia as a dysconnection syndrome. More generally, they echo the saying that “less is more”: The information processing performance of a network can be enhanced by a sparse or low-cost configuration with disproportionately high efficiency.
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Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Self-citationAn Energy Budget for Signaling in the Grey Matter of the Brain
Attwell, D., & Laughlin, S. B. (2001) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-200110000-00001 · Cited by 3,543
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553
Self-citationGamma rhythms and beta rhythms have different synchronization properties
Kopell, N., Ermentrout, G. B., Whittington, M. A., & Traub, R. D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.4.1867 · Cited by 1,072 · cited 1×
“The -band oscillation has recently attracted attention as a key coordinating frequency for large-scale networks because its periodicity, unlike that of the -oscillation, is long enough to coordinate neuronal activity in distant cortical sites that may be separated in time by an appreciable axonal conduction delay (24,…”
BackgroundSynaptic plasticity and dysconnection in schizophrenia
Stephan, K. E., Baldeweg, T., & Friston, K. J. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.10.005 · Cited by 841
Adaptive reconfiguration of fractal small-world human brain functional networks
Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606005103 · Cited by 814
Self-citationBroadband criticality of human brain network synchronization
Kitzbichler, M. G., Smith, M. L., Christensen, S. R., & Bullmore, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000314 · Cited by 527
Self-citation
Research works printed but not linked: 34
- 10.1073/pnas.0506806103 · DOI · Paper page
- 10.1103/PhysRevLett.87.198701 · DOI · Paper page
- 10.1016/j.tics.2004.07.008 · DOI · Paper page
- 10.1093/cercor/bhi016 · DOI · Paper page
- 10.1523/JNEUROSCI.3874-05.2006 · DOI · Paper page
- 10.1140/epjb/e2003-00095-5 · DOI · Paper page
- 10.1371/journal.pcbi.0020095 · DOI · Paper page
- 10.1177/1073858406293182 · DOI · Paper page
- 10.1176/appi.ajp.161.3.490 · DOI
- 10.1176/appi.ajp.160.12.2209 · DOI · Paper page
- 10.1162/jocn.2007.19.1.158 · DOI · Paper page
- 10.1016/j.brainres.2006.12.076 · DOI
- 10.1016/j.neuroscience.2007.09.009 · DOI
- 10.1016/j.neuroimage.2005.04.014 · DOI · Paper page
- 10.1016/j.neuroimage.2003.10.006 · DOI · Paper page
- 10.1001/archpsyc.60.9.889 · DOI · Paper page
- 10.1037/0894-4105.12.3.426 · DOI · Paper page
- 10.1016/S1388-2457(02)00197-9 · DOI
- 10.1023/A:1021124317706 · DOI
- 10.1073/pnas.94.22.12198 · DOI
- 10.3389/neuro.01.034.2008 · DOI
- 10.1371/journal.pcbi.1000169 · DOI
- 10.1073/pnas.0502366102 · DOI · Paper page
- 10.1073/pnas.0809511105 · DOI
- 10.1093/cercor/bhm069 · DOI · Paper page
- 10.1016/0920-9964(88)90039-4 · DOI
- 10.1176/ajp.149.7.890 · DOI · Paper page
- 10.1016/S0920-9964(97)00114-X · DOI · Paper page
- 10.1093/schbul/sbn070 · DOI · Paper page
- 10.1152/jn.00870.2007 · DOI
- 10.1073/pnas.0404944101 · DOI · Paper page
- 10.1093/cercor/bhh031 · DOI
- 10.1176/appi.ajp.160.4.709 · DOI · Paper page
- A Strehl, G Joydeep, Cluster ensembles—A knowledge reuse framework for combining multiple partitions. J Machine Learn Res 3, 583–617 (2002).
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The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Self-citationNetwork-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750
Self-citationExperimental and theoretical approaches to conscious processing
Dehaene, S., & Changeux, J.-P. (2011) · Neuron · doi:10.1016/j.neuron.2011.03.018 · Cited by 2,573
Rich-club organization of the human connectome
van den Heuvel, M. P., & Sporns, O. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3539-11.2011 · Cited by 2,354
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van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition
Wang, X.-J. (2010) · Physiological Reviews · doi:10.1152/physrev.00035.2008 · Cited by 1,939
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705
Self-citationFunction in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529
High-cost, high-capacity backbone for global brain communication
van den Heuvel, M. P., Kahn, R. S., Goñi, J., & Sporns, O. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203593109 · Cited by 781
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Fornito, A., Zalesky, A., Pantelis, C., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.090 · Cited by 716
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Bassett, D. S., Yang, M., Wymbs, N. F., & Grafton, S. T. (2015) · Nature Neuroscience · doi:10.1038/nn.3993 · Cited by 603
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Sakkalis, V. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.06.020 · Cited by 637
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Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614
Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00147 · Cited by 480
Self-citationCognitive effort drives workspace configuration of human brain functional networks
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Dehaene, S., Changeux, J.-P., & Naccache, L. (2011) · Research and Perspectives in Neurosciences · doi:10.1007/978-3-642-18015-6_4 · Cited by 209
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Doron, K. W., Bassett, D. S., & Gazzaniga, M. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216402109 · Cited by 153
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Godwin, D., Barry, R. L., & Marois, R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414466112 · Cited by 145
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van den Heuvel, M. P., van Soelen, I. L. C., Stam, C. J., Kahn, R. S., Boomsma, D. I., & Hulshoff Pol, H. E. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.06.007 · Cited by 127
Ginestet, C. E., & Simmons, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.030 · Cited by 112
O’Donoghue, S., Holleran, L., Cannon, D. M., & McDonald, C. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.11.015 · Cited by 109
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Ehrlich, S., Lord, A. R., Geisler, D., Borchardt, V., Boehm, I., Seidel, M., Ritschel, F., Schulze, A., King, J. A., Weidner, K., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22736 · Cited by 65
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Metadata
- Authors
- Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2009-07-14
- Volume
- 106
- Issue
- 28
- Pages
- 11747-11752
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2117415070
- Cited by
- 434
- References
- 7
- FWCI
- 13.70
- Type
- journal article
- Language
- en
- Open access
- green
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